Brain connectivity at the single neuron level can provide fundamental insights into how information is integrated and propagated within and between brain regions. However, it is almost impossible to adequately study this problem experimentally and, despite intense efforts in the field, no mathematical description has been obtained so far. Here, we present a mathematical framework based on a graph-theoretical approach that, starting from experimental data obtained from a few small subsets of neurons, can quantitatively explain and predict the corresponding full network properties. This model also changes the paradigm with which large-scale model networks can be built, from using probabilistic/empiric connections or limited data, to a process that can algorithmically generate neuronal networks connected as in the real system.
Model Type: Connectionist Network; Realistic Network
Model Concept(s): Connectivity matrix; Methods
Simulation Environment: MATLAB
Implementer(s): Giacopelli, Giuseppe [giuseppe.giacopelli at unipa.it]; Tegolo, Domenico [domenico.tegolo at unipa.it]
References:
Giacopelli G, Migliore M, Tegolo D. (2020). Graph-theoretical derivation of brain structural connectivity Applied Mathematics and Computation. 377